eigencore: Certified Partial Eigenvalue and Singular Value Computation

Computes the top-k singular triplets or eigenpairs of large sparse and structured matrices: the computation behind principal component analysis on big sparse data, spectral embeddings, and low-rank approximation. Every result carries a numerical certificate with residuals, a backward-error bound, orthogonality loss, and a pass/fail flag, and bounds that can only be estimated are reported as such rather than passed. Centered, scaled, and composed operators are solved through native 'C++' kernels without forming dense matrices. Drop-in replacements for the 'RSpectra' interface are included.

Version:	1.0.0
Depends:	R (≥ 4.1)
Imports:	Matrix, methods
Suggests:	bench, knitr, rmarkdown, RSpectra, irlba, rsvd, testthat (≥ 3.0.0)
Published:	2026-07-23
DOI:	10.32614/CRAN.package.eigencore (may not be active yet)
Author:	Bradley Buchsbaum [aut, cre, cph]
Maintainer:	Bradley Buchsbaum <brad.buchsbaum at gmail.com>
BugReports:	https://github.com/bbuchsbaum/eigencore/issues
License:	MIT + file LICENSE
URL:	https://bbuchsbaum.github.io/eigencore/, https://github.com/bbuchsbaum/eigencore
NeedsCompilation:	yes
SystemRequirements:	C++17
Materials:	README, NEWS
CRAN checks:	eigencore results

Documentation:

Reference manual:	eigencore.html , eigencore.pdf
Vignettes:	Certificates: reading the numerical evidence (source, R code) Get started with eigencore (source, R code) Generalized eigenvalue problems with eigencore (source, R code) PCA on sparse data without densifying (source, R code)

Downloads:

Package source:	eigencore_1.0.0.tar.gz
Windows binaries:	r-devel: not available, r-release: not available, r-oldrel: not available
macOS binaries:	r-release (arm64): eigencore_1.0.0.tgz, r-oldrel (arm64): eigencore_1.0.0.tgz, r-release (x86_64): eigencore_1.0.0.tgz, r-oldrel (x86_64): eigencore_1.0.0.tgz

Linking:

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